Top Data & Analytics Jobs

Provides pricing analytics support across product categories by developing pricing scenarios, reports, recommendations, and price updates. Conducts quality checks, validates pricing outputs, partners with Merchandising and Finance, resolves pricing issues, and identifies automation opportunities using analytical tools and data-driven methods.

Conduct quantitative research for automated trading strategies by developing mathematical models, translating algorithms into code, backtesting trading signals, analyzing unconventional data, and refining monetization systems. Collaborate with senior researchers during an 11-week post-doctoral internship while applying advanced probability, statistics, machine learning, and time-series techniques to financial markets.

Develop and deploy analytical and AI-enabled tools integrated with the Aladdin platform to support portfolio construction, investment research, relative value analysis, risk budgeting, due diligence, and portfolio monitoring. Partner with portfolio managers and engineering teams to identify opportunities, prototype solutions, manage delivery, and productionize scalable applications. Communicate insights clearly while applying knowledge of financial markets, asset classes, Python, SQL, and modern technology stacks.

Designs and manages enterprise finance data architectures, pipelines, transformations, semantic layers, retrieval indexes, and reusable data products. Partners with AI engineers on RAG, embeddings, vector databases, and data refresh processes. Evaluates data quality, lineage, security, privacy, and governance while developing synthetic or de-identified datasets. Implements monitoring, validation, exception handling, and experimentation datasets, and helps guide emerging data and AI architecture decisions.

Supports Finance and cross-functional teams by building data pipelines, improving data quality and governance, automating reporting, developing Power BI dashboards, applying AI and analytics, and contributing to financial models, forecasts, reconciliations, and executive insights.

Leads AI-enabled pricing and commercial excellence initiatives for John Deere Construction & Forestry. Applies analytics, competitive intelligence, pricing models, and scalable processes to improve deal quality, decision speed, revenue, and margins. Partners with pricing, sales, marketing, finance, revenue management, dealers, and leadership to translate complex data into actionable guidance and drive adoption of advanced pricing capabilities.

Develop sophisticated investment and trading models using statistical techniques, machine learning, and real-world datasets. Conduct end-to-end quantitative research, test investment hypotheses, collaborate with engineers, analyze market behavior, and communicate complex findings. Participate in academic seminars, reading groups, and industry conferences focused on statistics, machine learning, and data science.

Develop and test sophisticated investment models using scientific methods, machine learning, quantitative techniques, and large datasets. Conduct independent research, create investment ideas, collaborate with engineers, analyze real-world data, and communicate complex findings. Participate in research discussions, academic seminars, and industry conferences covering statistics, machine learning, and data science.

Develop and deploy advanced statistical and ML models on large-scale alternative datasets to generate investable signals, partner with investment teams, evaluate data quality, and drive novel data-driven research with commercial impact.

Develop fraud risk strategies and analytical solutions using complex datasets, SAS, SQL, Python, and machine learning. Monitor fraudulent behaviors, create risk metrics and dashboards, design loss-mitigation products, and present recommendations to senior stakeholders. Manage strategic initiatives, collaborate with product and operations teams, and supervise analytics employees through delegation, performance management, hiring, and mentorship.

Conduct quantitative research for proprietary trading strategies by analyzing high-frequency market data, developing statistical and machine-learning models, building simulations and backtesting tools, and evaluating model performance. Collaborate with traders and developers to validate algorithms, improve pricing and execution, assess production readiness, prioritize research initiatives, and integrate validated models into low-latency trading systems. Mentor graduate researchers and interns.

Leads an enterprise organization spanning data and AI governance, architecture, and product management. Establishes governance frameworks, architectural standards, risk controls, and responsible AI practices while developing scalable data and AI products. Translates enterprise strategy into investment portfolios, roadmaps, operating models, and measurable outcomes. Advises executives, aligns business and technology stakeholders, manages budgets and vendors, and builds a multidisciplinary leadership organization focused on adoption, reuse, quality, compliance, and business value.

Build and maintain analytics data systems and pipelines that capture, transform, and integrate product usage data at scale. Deliver accurate insights and visualizations to inform product decisions, collaborate cross-functionally, and apply AI tools to improve analytics and workflows.

Leads advanced analytics, reporting, scalable data models, dashboards, and data pipelines supporting Business and Marketing teams. Partners with senior stakeholders and AI Engineering to implement AI and machine learning workflows, predictive insights, and automation. Develops documentation, drives analytics engineering best practices, and mentors team members. Requires strong SQL, Python, APIs, data modeling, stakeholder communication, project leadership, and blockchain expertise.

Leads BI and analytics development by designing, optimizing, and governing dashboards, analytical models, semantic layers, SQL queries, and stored procedures across Cognos, QlikView, and Power BI. Collaborates with business, data science, visualization, and engineering teams to prepare data, develop machine learning or exploratory analytics solutions, automate production workflows, and deliver actionable insights. Provides technical leadership, mentors team members, establishes standards, reviews work, and promotes reusable components and best practices.

Leads complex fulfillment-center analytics and applied AI initiatives from problem definition through production adoption. Builds scalable data models, pipelines, inspection tools, root-cause analysis, predictive solutions, and self-service analytics. Partners with Product, Engineering, Data Science, and Operations, establishes engineering standards, conducts reviews, mentors junior engineers, and communicates recommendations to senior stakeholders. Requires strong SQL, Python, cloud data, machine learning, visualization, testing, and software engineering expertise.

Lead data architecture across more than 100 applications, establishing architecture principles, database standards, data modeling practices, governance frameworks, and data exchange patterns. The role drives migration to strategic cloud data lakes, reduces duplicate data platforms, defines target-state architectures, serves as a subject matter expert, and mentors junior architects. It requires extensive experience with cloud databases, data lakes, data governance, data quality, analytics, machine learning, and distributed enterprise technology environments.

Deliver business intelligence and performance insights for institutional sales teams. Build sales enablement tools, AI-enabled workflows, KPI frameworks, and scalable data models. Analyze complex datasets, prototype statistical or machine learning models, ensure data governance, and translate findings into recommendations for senior stakeholders while partnering with technology and cross-functional teams.

Design, build, deploy, and support scalable cloud data solutions, pipelines, platforms, and applications for enterprise risk management. The role uses Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Responsibilities include data modeling, distributed and streaming workloads, security controls, architecture standards, observability, operational efficiency, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Data Engineering Interns will develop and maintain scalable data pipelines, APIs, data products, and enterprise platforms. Responsibilities include data ingestion, transformation, validation, governance, quality monitoring, metadata management, cloud and distributed computing, CI/CD, testing, and observability. Interns will also use approved generative AI tools, support AI-enabled engineering workflows, and collaborate with engineers, architects, product teams, and business stakeholders. The 10-week hybrid program includes training, mentorship, and potential consideration for a subsequent full-time Technology Analyst role.

Data Engineering Interns will build and maintain scalable data pipelines, APIs, data products, and enterprise platforms. Responsibilities include data ingestion, transformation, validation, governance, quality monitoring, metadata management, cloud and distributed computing, CI/CD, testing, and observability. Interns will also use approved AI tools, support AI-enabled engineering workflows, and collaborate with engineers, architects, product teams, and business stakeholders. The 10-week program includes technical training, mentorship, and networking, with potential consideration for a full-time Technology Analyst role.

Data Engineering Interns will build and maintain scalable data pipelines, APIs, data products, and enterprise platforms. Responsibilities include data ingestion and transformation, software testing, CI/CD, cloud and distributed computing, data quality, governance, metadata, lineage, and reliability initiatives. Interns will also use approved AI tools, support AI-enabled engineering workflows, collaborate with technical and business teams, and participate in training, mentorship, and networking during the 10-week program.

Leads an analytics team supporting Home Lending Operations by framing business problems, delivering analysis, building reporting, defining data requirements, and advising senior stakeholders. Develops AI and machine learning use cases to optimize operations and customer experiences, establishes analytics quality standards, supports experimentation and product measurement, and partners cross-functionally with Operations, Product, Technology, Finance, Strategy, Business Intelligence, and Modeling teams.

Manage data analytics for PULSE Risk & Compliance by analyzing large datasets, identifying transaction anomalies and risk exposure, validating models, improving reporting and processes, and supporting compliant data strategies. The role involves Python-based programming, BI, database and data-storage design, strategic testing, cross-functional collaboration, project leadership, and people management in a hybrid environment.

Leads quantitative analytics initiatives that transform complex data into actionable insights and scalable, governed solutions. Responsibilities include stakeholder partnership, self-service analytics development, customer journey analysis, AI and automation application, executive presentations, production reporting support, regulatory compliance, process improvement, and mentoring junior analysts.